Anna Queralt is a Senior Researcher at the Barcelona Supercomputing Center. Her research and professional interests revolve around distributed data management for high performance computing, big data, and edge to cloud applications. She is a member of the ELASTIC project, working on the distributed storage that serves other components in ELASTIC. In this interview, Anna talks about her experience as a woman in STEM and as a member of the ELASTIC team.

• How did you become interested in engineering? What influenced your decision in taking this career path?

I have been kind of interested in science and engineering since I was a little girl. I have always enjoyed solving problems and understanding why and how things work. So the choice of this career path was quite a natural decision, although I had no experience with technology at that time. Many of my classmates in the first courses at university already had computers since they were kids, and they even knew how to program, but this was not my case. I chose Computer Science basically because I wanted to study something related to maths and logics, and that could provide interesting and diverse professional opportunities at the same time.

Although the fact of not having any previous experience was not an obstacle, I think the decision will be much easier for nowadays girls, since technology is already part of their lives, and they even learn notions of technology very early at school, as I have seen with my daughters. And this is a very good thing, because they will probably be less reluctant to choose career paths related to technology or engineering, as they will not see it as something inaccessible or strange, or something only for boys.

• How has your experience as a woman studying and working in STEM been? Have you faced any challenges?

Studying and working in STEM has been a great experience, since it poses new challenges almost every day, and one doesn’t become tired of doing always the same. As a woman, I haven’t had any problem in general, and I have always felt comfortable regardless the level of studies or the professional context, despite being the only female in many cases. In fact, I recently became the leader of a small research group, with the rest of members being males (and my boss is a woman, too). So although we, women, are not many in this field, this doesn’t prevent us from growing professionally.

However, throughout my career I have occasionally found some difficulties with some particular persons (either man or woman), and sometimes I had to prove more than my male colleagues. However, I am afraid that, unfortunately, this does not only happen in STEM, but more generally, regardless of the career. It is true that it is becoming less common, but still happens sometimes.

I think that women in STEM provide a very valuable contribution to an environment predominated by men, bringing a set of complementary qualities and skills. Generally, we are more reflective rather than impulsive, we are more meticulous and organized, and we have a tendency to resolve conflicts, thus avoiding uncomfortable situations. We are usually more empathic, too, which allows us to detect and solve possible problems before it is too late, and naturally make people feel comfortable, which at the end leads to better results. Of course, these characteristics are not exclusive of women (and not all women are the same, either), but they are more prominent in women.

• What are you working on in the ELASTIC project and how has this experience been so far?

In ELASTIC, I am in charge of the distributed storage, which provides data management functionalities to other components. On the one hand, it manages data from the use cases as part of the Distributed Data Analytics Platform (DDAP), supporting the real-time data analytics needs at the edge, and handling the data to the historical analytics part of the DDAP. On the other hand, it supports the Non-Functional Requirements tool (NFR) by storing and distributing different kinds of data collected within ELASTIC, in order to manage the scheduling of tasks according to the non-functional requirements.

One thing about ELASTIC that I like very much is that it gives us the opportunity to have an impact on daily lives of people. Although this is the final goal of any research project, such an impact is not always as evident or direct as in ELASTIC, where the final users are regular citizens.

The experience of working in ELASTIC has been great so far, but unfortunately, due to COVID-19, we have not been able to meet face to face for a long time. However, we are constantly in touch with on-line meetings and keep progressing well. I hope that the situation improves soon, so that at least we can travel to Florence to see the results of our work in person.

• What message would you give to young girls and women who are interested in pursuing a career in STEM?

I would tell them that if they want to do something, whatever it is, they can do it. No one should renounce to a goal just because of his or her sex, in the same way that it does not make sense to discard a career because of one’s ethnicity. It only depends on them.

We were more than happy to contribute to the European Big Data Value Forum (EBDVF 2020) this year with a parallel session under the “Smart Society” Track dedicated to the innovative ELASTIC software architecture. Thanks to our panelists and attendees, the session gave a holistic overview of the project’s work towards a next generation mobility system and received a lot of attention and positive feedback!

Under the title “Next generation smart mobility systems, leveraging extreme-scale analytics over a novel elastic software architecture”, the discussion focused on the ELASTIC software ecosystem, capable of exploiting the distributed computing capabilities of the compute continuum of the smart city, while guaranteeing additional properties, such as real-time, energy, communication quality, and security.

We further talked about how this design will be adopted and tested in a real-life mobility use case in the public tram network of Florence in Italy, aiming to improve safety, efficiency and maintenance of the transportation vehicles. The presentations were followed by a lively Q&A time when attendees had the opportunity to ask questions and interact with the panel.

Find details on the panelists and download their presentation slides below:
•    Eduardo Quiñones (ELASTIC Project Coordinator, BSC): Overview of the ELASTIC project and the concept of elasticity
•    Jürgen Assfalg (ICT Manager, Città Metropolitana di Firenze): Smart mobility use case in the city of Florence
•    Anna Queralt (Senior Computer Scientist, BSC): Distributed platforms for extreme data analytics    
•    Elli Kartsakli (Senior Researcher, BSC): A novel software architecture for analytics workload distribution whilst fulfilling real-time, energy, communication and secure properties

Written by Cristóvão Cordeiro, R&D Project Manager, at SixSq

With the increasing adoption of ubiquitous computing, terms like Cloud, Edge, Fog and IoT are now more popular than ever. Recent news has shown us that data is everywhere - it comes from nature, from computer analysis, from us humans… and it is being used to generate information that turns out to be critical in the most diverse sectors, from education, to healthcare, automotive and even politics.

Recent forecasts estimate that there will be approximately 41.6 billion connected IoT devices, generating 79.4 zettabytes (ZB) of data, by 2025. To put this number into perspective, one would need to binge watch 3.5 billion years of YouTube videos in order to consume this much data.

Even though data is the new gold, without a purpose, it is just white noise. Data needs processing, and this ever-increasing deluge raises new computing concerns with respect to: cost, network latency & reliability, and privacy. This is where ELASTIC's Edge-to-Cloud infrastructure comes into play.

The Edge-to-Cloud data flow model

From a management perspective, ELASTIC is putting together an intelligent analytics-driven orchestration engine which is capable of reaching out to both edge and cloud infrastructures, to deploy and distribute workloads closer to the data sources.

Take ELASTIC's smart mobility use cases as an example: all data which is critical for assisting public transport drivers is generated at the edge, thus it is only logical to process that same data at its source, without saturating the network, only keeping the actual information and results of the computation for later analysis. By doing so, ELASTIC is dramatically reducing the cost of the network, while ensuring that whatever applications run at the edge are not impacted by unexpected network delays. Plus, since Artificial Intelligence is being used for processing heavy data (like video feeds), the self-contained edge workloads ensure that the privacy bubble does not burst, and only anonymized data can travel via public networks up to the cloud.

In fact, ELASTIC's Software and Fog Architectures have been designed in such a way that they build on top of proven technologies, providing benefits for the Edge-to-Cloud and Data-to-Information paradigms. In particular, ELASTIC's Fog Computing architecture encompasses the best of two Edge Computing software appliances – KonnektBox and NuvlaBox – to deliver the best possible conditions for a seamless and performant transformation of data into useful information:

- Container-based Software Stack: these edge computing appliances are built with containers, for containers. In ubiquitous computing, monolithic and tailored applications are no longer an option. Via the use of containers, ELASTIC provides support for portable, stateless and loosely-coupled applications, thus widening the applicability of the framework to endless use cases.
- Proactive Monitoring: monitoring is critical. Moreover, when the availability of the edge devices cannot be guaranteed (due to mobile environments, unreliable edge networks, etc.), telemetry and monitoring information of the edge system becomes paramount in the identification of problems, such as underperformance and intrusion. Both the KonnektBox and NuvlaBox are equipped with edge monitoring agents capable of providing the right metrics for assessing the state of both the edge devices and the underlying applications.
- Distributed Storage: only keep what's necessary. It would be impossible to persist all the raw data coming from IoT sensors. Thus, the ELASTIC Fog Architecture comprises the mechanisms to not only store but also to flag and synchronize important data throughout the edge and up to the cloud.
- Data Routing: if it is the information that matters, then why store everything? Instead, let the application consume the intended raw data in flight, and discard everything else. The KonnektBox and NuvlaBox provide a functionality to allow the ELASTIC use cases to build applications without the need to individually and natively interact with every data source (sensor). The data router abstracts this interaction, by automatically discovering IoT peripherals and relaying their raw data through an MQTT broker, to which any authorized ELASTIC application can subscribe.

Written by Flavia Gaudio, engineer at Gestione Ed Eserizio del Sistema Tranviario SPA (GEST)

The maintenance of the Florence Tramway System is a challenging task for GEST. The infrastructure is stressed with trams rolling for around 7000 km everyday along the 3 existing lines. In this scenario the “preventive” approach, currently adopted in maintenance, is based mostly on industry averages and best practices, where different kind of triggers determine when to do service to the equipment; these triggers can be specific time interval expiration, defined traveled distance thresholds or the occurrence of specific conditions.

The preventive approach implies the collection, analysis and management of a big amount of information for each component under maintenance, such as equipment age, dates of inspection and service records. Therefore, considering also the future tramway line extensions, an increasing effort in terms of costs, resources and time need to be allocated on these tasks.

Monitoring of key indicators, such as failure events frequencies, operational costs and root causes identification, represent the actual basis on which short-term decisions are taken and long-term strategies are planned.

A step forward can be achieved by the transition from a “preventive” to a “predictive” maintenance approach where processes are refined and intervention frequencies are tuned on the basis of indicators “felt” from the system. This approach implies a revamping of the current equipment with a higher level of technology, such as the installation of IoT sensors and other software and hardware tools needed to collect information considered representative of the true and most updated picture of the system utilisation.

Starting from manuals and guidelines, procedures and activity scheduling can be, thus, optimised on the basis of the actual utilisation of the equipment, focusing also on environmental factors or behaviours that may affect the state of components in an unexpected way

Track affected by severe wear along Tramway Line 1 in Florence. Picture taken before maintenance intervention.

Predictive maintenance can be useful both for validating the current maintenance schedule and fine tuning of the planned strategies by means of: indicators trends monitoring, unexpected faults detection in the early stages of their appearance, and correlation, when possible, with habits in infrastructure and tram usage that could impact significantly on the duration of specific components. Therefore, the maintenance interventions are driven toward the equipment that encounters a significant or anomalous wear rate that may soon brought to a failure.

In the Predictive Maintenance use case, driven by GEST, the attention has been focused mostly on data related to track consumption and energy absorption.

Track and tram components stressing and energy absorption during daily service are crucial factors. Compromising even one of them may bring a sudden and heavy decrease of both safety and efficiency levels and, in worst cases, interruption of the transportation service.

Track profiles are sampled using laser based sensors mounted on the maintenance vehicles, owned by GEST, while energy related data are sampled runtime from specific trams in service equipped with a dedicated unit that captures them directly from the internal system of the tram.

Laser blade generated by sensors for track profile detection while maintenance vehicle is moving.

Once collected, these data are forwarded to the ELASTIC software infrastructure by means of edge devices mounted on board. The exploitation of elastic computing resources distributed both at fog and cloud level can make this data to then be organised, analysed and presented in a useful way by means of a dashboard.

GEST has driven the definition of the analytics requirements that drive the implementation of the ELASTIC dashboard so that the maintenance team can be supported in:
•    Defect detection and classification
•    Comparison of the collected data with the theoretical/expected ones
•    Notifications in case of parameters out of tolerances
•    Parameters trend follow up
•    Unexpected worsening of parameters identification and alerting
•    Definition of maintenance intervention efficacy after its execution

The idea of “feeling” the system through sensors and edge devices establishes a brand-new link between the existing tramway network in Florence and an innovative software infrastructure, whose computing resources speed up innovation via the predictive maintenance approach.

Rita Sousa is a master's degree student in Information and Knowledge Systems at the School of Engineering of the Polytechnic Institute of Porto. Her research and professional interests revolve around Artificial Intelligence, Machine Learning, and Internet of Things. She is a member of the ELASTIC project, working on the Non-functional requirements tool. In this interview, Rita talks about her experience as a woman in STEM and as a member of the ELASTIC team.

• How did you become interested in engineering? What influenced your decision in taking this career path?

When I was a kid, I always loved to design and build things and I always wanted to study hard to be able to build machines to solve problems. When I grew up, I discovered that I really liked Mathematics and Geometry, but not so much Physics. And probably here comes the peculiar way of deciding on a career. I knew I wanted to be an engineer, so I wondered ‘what engineering field involved less theoretical physics’? It turned out that was Computer Engineering. Since I had no previous experience in this field, I was not sure what would follow. I can say now that I totally do not regret that decision.

• How has your experience as a woman studying and working in STEM been? Have you faced any challenges?

It has been great and at the same time challenging as some things are difficult to understand. However, there are thousands of people working in STEM, and it is, therefore, practically impossible not to have someone to help you overcome the small obstacles, as long as you look for that help yourself. Since I was a kid, I had different studying and sports activities with various communities, so luckily I never had any major problems adapting to the environment that I was in, regardless of what I tackled each time.

• What are you working on in the ELASTIC project and how has this experience been so far?

I am working on the Non-functional requirements tool, more specifically, in the time and energy dimensions. I deal with the acquisition of metrics to evaluate, e.g. system health and I am implementing some heuristics for resource management. I consider my experience in ELASTIC very positive. It is the first time I work in a European project, and everyone, even from outside of my own team, are available to help me.

• What message would you give to young girls and women who are interested in pursuing a career in STEM?

STEM subjects are exciting. Do not be afraid to pursue any career, even if you have some doubts initially. With hard work, you will be able to succeed. In reality, there is no professional career that has gender restrictions. Every year, this notion is being contradicted, and more and more girls are joining the STEM fields.

Written by Jürgen Assfalg, ICT Manager at Città Metropolitana di Firenze

An elastic software architecture is expected to enable next generation smart mobility

During the ages, urban areas have attracted an ever increasing number of people. The growth of cities and metropolitan areas has led to an increased mobility demand of people and freights, among and inside those areas. For a long time this demand has been faced by building new infrastructures. However, in recent years, rising attention for sustainability – environmental, economical, and social – has forced cities to evaluate and adopt new approaches, aimed at optimising the performance of existing infrastructures as well as at promoting the shift towards more efficient transportation modes.

In the above outlined context, ICT is gaining a central role, as it allows to implement comprehensive control systems able to effectively manage mobility: on the one hand, widespread sensor networks monitor both mobility demand and offer; on the other hand, control logics and actuators support enforcement of policies devised by local governments, transportation agencies and other stakeholders.

Consequently, the metropolitan government of Florence, the City of Florence and other operators have been increasingly deploying Intelligent Transportation Systems to manage mobility in the area, thus developing a solid and extensive smart mobility infrastructure. Also, in 2019 the Metropolitan City of Florence has adopted its Sustainable Urban Mobility Plan (SUMP), where ITS and MaaS have a key role.

While initially each single transportation mode has been addressed separately, more recently the focus has been moved on interaction between the public and private transport, which is one of the use cases of the ELASTIC project. Interaction between the two transportation modes is worth of being investigated at least from two perspectives, namely transportation network performance (or service levels) and safety. From the point of view of the technical implementation, the former mainly relies on computationally intensive data analytics, the latter requires to deliver real-time alerts and information.
 

At intersections and crossings, different transportation modes – tramway (blue), cars (red), pedestrians (yellow) – interact in various ways, possibly affecting performance of the transportation networks and/or safety of people.

Such a use case, massively relying on IoT and featuring a variety of functional and non functional requirements, barely fits within edge or cloud architecture, which fulfil only a subset of requirements of the envisaged use case. A new computing paradigm, named fog computing, has recently appeared to efficiently combine the benefits of edge and cloud computing. In that regard, ELASTIC is developing a fog platform capable of meeting the wider spectrum of requirements, set by forthcoming assisted and autonomous driving scenarios relying on a smart coordination of edge and cloud technologies, and vehicle-to-anything communication (V2X) technologies.

The distributed data analytics platform (DDAP) of the ELASTIC architecture processes data from a variety of sources (sensors, trams, vehicles, etc.) and identifies potentially hazardous situations, which are signalled to drivers through V2X communication devices.

Additional information

Città Metropolitana di Firenze: http://www.cittametropolitana.fi.it/
Jürgen Assfalg: https://www.linkedin.com/in/jurgen-assfalg/

Viola Sorrentino is an engineer at Thales Italia S.p.A. Her research and professional interests revolve around telecommunications, obstacles detection and system engineering. She is a member of the ELASTIC project, working on the development of the NGAP (Next Generation Autonomous Positioning) and the ADAS (Advanced Driving Assistant System) use cases. In this interview, Viola talks about her experience as a woman in STEM and as a member of the ELASTIC team.

• How did you become interested in engineering? What influenced your decision in taking this career path?

During my education path, I realized I was attracted by solving problems through designing and planning, while following logical and mathematical steps. So I challenged myself with an engineering university career and I discovered my passion for electronics and electromagnetics systems.

I have always liked building and assembling components in the domestic environment and from there it was an easy step to be deeply engaged with technology evolution and innovation. Then moving to the university and working environment, I entered in a virtual circle of satisfaction and new discoveries that I still enjoy very much.

• How has your experience as a woman studying and working in STEM been? Have you faced any challenges?

I had a great experience studying and working in STEM. I am still learning a lot every day. At the university, my challenge was to organise and plan my activities to achieve my master degree on time. I was requested to work on the ongoing ELASTIC project since my first working days, so it was a significant challenge to conquer trust and respect from my senior colleagues in a short term; everything moved very well and fast and I am excited about that.

• What are you working on in the ELASTIC project and how has this experience been so far?

I am working on the development of the NGAP and ADAS systems together with my colleagues. I am focused on the design of the HW prototype and cables necessary for the installation on the tram. Moreover, I had the opportunity to study different types of sensors in order to select the models that fit with the operating environment and scenarios of the NGAP and ADAS. We are integrating the chosen sensors in the architecture designed by THALIT. Last but not least, I plan and schedule the activities to achieve the milestones and related tasks foreseen by the ELASTIC project.

The experience so far has been great. I have discovered new technology boundaries and worked with innovative tools. The ELASTIC team has members coming from six different nations, so it is an engaging multinational and multicultural environment.

• What message would you give to young girls and women who are interested in pursuing a career in STEM?

I have a straightforward message for young girls and women who are interested in pursuing a career in STEM: YES, WE CAN! My personal career so far shows that if you work hard and achieve great results, no one will have a reason to stop you.